Top 5 reasons why data integration projects fail - and how to avoid failure
By Henning Lund - June 16, 2016
Data integration projects are not much different from other software implementation projects: although the statistics are not quite as tragic as for ERP or CRM implementation projects, data integration projects also have an implementation failure risk attached to them. In my experience, it is rarely purely technical issues with the integration solution itself that make data integration projects fail.
What are the typical pitfalls of data integration projects?
Failure to recognize the limitations of pre-developed models. Although many vendors provide today pre-configured data integration solutions, they are very rarely to be considered as pure “plug and play”. Your company’s specificities and your systems’ even minimal customizations could just mean some extra work in order to perform the integration. Don’t get me wrong now, I think that pre-configured solutions are great. We work a lot for pre-configured solutions and I have performed many successful integrations using pre-setup models. It is just important to understand that in most cases they are just a great starting point and it is crucial that you select a data integration tool that can be flexible enough to fit your company’s data model approach.
Lack of broad executive involvement. Although IT has a leading part in your data integration project, it would be a huge mistake not to involve more of your executives. Executive level buy-in drives cooperation with data owners, user adoption and is simply vital. Why? Because your data integration project will not only affect IT, it will impact broadly in your organization. Remember that the implementation is about sharing data and automating processes. In my experience for example, the best CRM-ERP integration projects involve a CIO or IT director, but also include CEO-level support and involvement of Sales and Marketing top management.
Lack of long-term strategy. Many ERP or CRM vendors have developed a one-off integration between systems for their customers. Some companies have done it for themselves. Although this might seem as a good idea in the beginning as they have a good understanding of the company’s processes and data models, this can prove to be a mistake in the long run. Why? Because in reality, these integration solutions are rarely developed with a full long-term future consideration. What will happen when the integrated systems get upgraded? What if you wish to extend the use of your integration tool and integrate with other systems? When you choose your data integration solution, always make sure that it is future proof and can keep being used when the integration constellation changes. Custom-made interfaces generally require development, which makes upgrades and maintenance less flexible and more expensive.
Political resistance. Data management can be a touchy issue and some pisions of your company might believe that they own the data in their part of the system – and might therefore be unwilling to allow another system to access (let alone change!) what they consider to be their critical information. This is where a broad executive support (as mentioned in point 2) comes handy.
Poor data quality. This is in my mind the most important and critical one. To put it bluntly, if you put garbage in one end, you will get nothing but garbage out at the other end. Data integration projects without a company-wide focus on data quality before, during and after the data integration implementation project will inevitably fail. At the end of the day, good data quality is what will ensure user-adoption and consequently success of your data integration project. Give your users poor data quality and they will start to distrust the data in the system and will start going back to old, unproductive processes. The best data integration projects always have a dedicated data quality champion.
With these 5 points in mind, you should be off to a good start to select the right solution but also to prepare your organization and get their buy-in, as both are equally important in order to avoid data integration project failure.
About the author
With over 25 years’ experience in strategically propelling businesses forward, Henning is considered a business development entrepreneur with a passion for transforming businesses, sales and marketing operations through out-of-the-box thinking, concepts building and process automation to improve overall performance and scalability.